New algorithms for processing and peak detection in liquid chromatography/mass spectrometry data

Two new algorithms for automated processing of liquid chromatography/mass spectrometry (LC/MS) data are presented. These algorithms were developed from an analysis of the noise and artifact distribution in such data. The noise distribution was analyzed by preparing histograms of the signal intensity...

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Vydané v:Rapid communications in mass spectrometry Ročník 16; číslo 5; s. 462 - 467
Hlavní autori: Hastings, Curtis A., Norton, Scott M., Roy, Sushmita
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Chichester, UK John Wiley & Sons, Ltd 01.01.2002
Predmet:
Algorithms
Chromatography, Liquid - statistics & numerical data
Data Interpretation, Statistical
Mass Spectrometry - statistics & numerical data
Nonlinear Dynamics
ISSN:0951-4198, 1097-0231
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Popis
Shrnutí:Two new algorithms for automated processing of liquid chromatography/mass spectrometry (LC/MS) data are presented. These algorithms were developed from an analysis of the noise and artifact distribution in such data. The noise distribution was analyzed by preparing histograms of the signal intensity in LC/MS data. These histograms are well fit by a sum of two normal distributions in the log scale. One new algorithm, median filtering, provides increased performance compared to averaging adjacent scans in removing noise that is not normally distributed in the linear scale. Another new algorithm, vectorized peak detection, provides increased robustness with respect to variation in the noise and artifact distribution compared to methods based on determining an intensity threshold for the entire dataset. Vectorized peak detection also permits the incorporation of existing algorithms for peak detection in ion chromatograms and/or mass spectra. The application of these methods to LC/MS spectra of complex biological samples is described. Copyright © 2002 John Wiley & Sons, Ltd.
Bibliografia:ArticleID:RCM600
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ark:/67375/WNG-KT0C8047-K
ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0951-4198
1097-0231
DOI:10.1002/rcm.600